---------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------
      name:  <unnamed>
       log:  /Users/pamelaban/Dropbox/newspapers_power/PSRM_replication/dataverse_upload/log_stata_files.log
  log type:  text
 opened on:  19 Apr 2017, 14:53:23

. 
. ********************************************************************************
. * This do-file produces the files to replicates the main tables and figures
. * in "How Newspapers Reveal Political Power" (Ban, Fouirnaies, Hall, Snyder)
. *
. *
. * -- Produces the .tex files for the following tables:
. *               Table 1: News Coverage Before, During, and After Leadership Term
. *               Table 2: Impact of Switch from Mayor-Countil to Council-Manager
. *                                City Government (also Table A.4)
. *
. * -- Produces the .dta files to be used to plot figures; next step in R with
. *        the code make_file_graphs.R
. *
. * November 20, 2016
. ********************************************************************************
. 
. 
. set more off

. 
. local make_committees   = 1

. local make_leader               = 1

. local make_mayors               = 1

. local make_rtaa                 = 1

. local make_party_committee = 1

. 
. 
. 
. ********************************************************************************
. * Committee Rankings, 1949-1973
. ********************************************************************************
. 
. 
. if `make_committees'==1 {
. 
.         use hits_housecommittees.dta, clear
.         keep if year>=1949 & year<=1973
(76 observations deleted)
.         collapse (sum) agriculture appropriations armed_services banking education energy foreign_affairs government_operations house_administration judiciary merchant_marine natural_resources post_office public_works rules science standards_of_official
> _conduct veterans_affairs ways_and_means
. 
.         egen total_hits = rowtotal(agriculture appropriations armed_services banking education energy foreign_affairs government_operations house_administration judiciary merchant_marine natural_resources post_office public_works rules science standards
> _of_official_conduct veterans_affairs ways_and_means)
.         
.         rename agriculture c_agriculture
.         rename appropriations c_appropriations
.         rename armed_services c_armed_services
.         rename banking c_banking
.         rename education c_education
.         rename energy c_energy
.         rename foreign_affairs c_foreign_affairs
.         rename government_operations c_government_operations
.         rename house_administration c_house_administration
.         rename judiciary c_judiciary
.         rename merchant_marine c_merchant_marine
.         rename natural_resources c_natural_resources
.         rename post_office c_post_office
.         rename public_works c_public_works
.         rename rules c_rules
.         rename science c_science
.         rename standards_of_official_conduct c_standards_of_official_conduct
.         rename veterans_affairs c_veterans_affairs
.         rename ways_and_means c_ways_and_means
.         
.         reshape long c_, i(total_hits) j(committee) string
(note: j = agriculture appropriations armed_services banking education energy foreign_affairs government_operations house_administration judiciary merchant_marine natural_resources post_office public_works rules science standards_of_official_conduct veter
> ans_affairs ways_and_means)

Data                               wide   ->   long
-----------------------------------------------------------------------------
Number of obs.                        1   ->      19
Number of variables                  20   ->       3
j variable (19 values)                    ->   committee
xij variables:
c_agriculture c_appropriations ... c_ways_and_means->c_
-----------------------------------------------------------------------------
.         
.         replace c_ = c_/total_hits
(19 real changes made)
.         rename c_ rel_hits
.         drop total_hits
.         
.         gsort -rel_hits
.         gen rank = _n
.         
.         replace committee = subinstr(committee,"_"," ",.)
(11 real changes made)
.         replace committee = upper(committee)
(19 real changes made)
.         replace committee = "ENERGY AND COMMERCE" if committee=="ENERGY"
(1 real change made)
.         replace committee = "EDUCATION AND LABOR" if committee=="EDUCATION"
(1 real change made)
.         keep committee rank
.         sort committee
.         
.         tempfile committee
.         save `committee', replace
(note: file /var/folders/61/8_4x2m71723620zdy7x618300000gn/T//S_03391.000001 not found)
file /var/folders/61/8_4x2m71723620zdy7x618300000gn/T//S_03391.000001 saved
.         
.         import delimited "groseclose_stewart_81st_93rd.csv", varnames(1) clear
(2 vars, 19 obs)
.         replace committee = upper(committee)
(19 real changes made)
.         rename rank GS_rank
.         merge 1:1 committee using `committee'

    Result                           # of obs.
    -----------------------------------------
    not matched                             0
    matched                                19  (_merge==3)
    -----------------------------------------
.         
.         drop _merge
.         
.         corr rank GS_rank
(obs=19)

             |     rank  GS_rank
-------------+------------------
        rank |   1.0000
     GS_rank |   0.7404   1.0000

.         
.         export delimited using "for_committees_r_graph.csv", replace
(note: file for_committees_r_graph.csv not found)
file for_committees_r_graph.csv saved
. }

. 
. 
. *****************************************************************************************
. * News Coverage of Speakers of the House, Before, During, and After Speakership
. * Table 1: News Coverage Before, During, and After Leadership Term
. *****************************************************************************************
. 
. if `make_leader'==1 {
. 
.         *Table comparing means
.         use hits_leadernames, clear
.         collapse (sum) randall keifer carlisle reed     crisp henderson cannon clark gillett longworth  garner rainey byrns     bankhead rayburn martin mccormack albert halleck garrett snell mann, by(year)
.         qui foreach var in randall keifer carlisle reed crisp henderson cannon clark gillett longworth  garner rainey byrns     bankhead rayburn martin mccormack albert halleck garrett snell mann {
.         reshape long hits, i(year) j(name) string
(note: j = albert bankhead byrns cannon carlisle clark crisp garner garrett gillett halleck henderson keifer longworth mann martin mccormack rainey randall rayburn reed snell)

Data                               wide   ->   long
-----------------------------------------------------------------------------
Number of obs.                      293   ->    6446
Number of variables                  23   ->       3
j variable (22 values)                    ->   name
xij variables:
  hitsalbert hitsbankhead ... hitssnell   ->   hits
-----------------------------------------------------------------------------
.         gen period = .
(6,446 missing values generated)
.         replace period=1 if year>=1871 & year<1876 & name=="randall"
(5 real changes made)
.         replace period=2 if year>=1876 & year<=1881 & name=="randall"
(6 real changes made)
.         replace period=3 if year > 1881 & year<=1886 & name=="randall"
(5 real changes made)
.         
.         replace period=1 if year>=1876 & year<1881 & name=="keifer"
(5 real changes made)
.         replace period=2 if year>=1881 & year<=1883 & name=="keifer"
(3 real changes made)
.         replace period=3 if year > 1881 & year<=1886 & name=="keifer"
(5 real changes made)
.         
.         replace period=1 if year>=1878 & year<1883 & name=="carlisle"
(5 real changes made)
.         replace period=2 if year>=1883 & year<=1889 & name=="carlisle"
(7 real changes made)
.         replace period=3 if year > 1889 & year<=1894 & name=="carlisle"
(5 real changes made)
.         
.         replace period=1 if year>=1884 & year<1889 & name=="reed"
(5 real changes made)
.         replace period=2 if ([year>=1889 & year<=1891] | [year>=1895 & year<=1899]) & name=="reed"
(8 real changes made)
.         replace period=3 if ([year>1899 & year<=1904] | [year>1891 & year<1895]) & name=="reed"
(8 real changes made)
.         
.         replace period=1 if year>=1886 & year<1891 & name=="crisp"
(5 real changes made)
.         replace period=2 if year>=1891 & year<=1895 & name=="crisp"
(5 real changes made)
.         replace period=3 if year>1895 & year<=1900 & name=="crisp"
(5 real changes made)
.         
.         replace period=1 if year>=1898 & year<1903 & name=="henderson"
(5 real changes made)
.         replace period=2 if year>=1899 & year<=1903 & name=="henderson"
(5 real changes made)
.         replace period=3 if year>1903 & year<=1908 & name=="henderson"
(5 real changes made)
.         
.         replace period=1 if year>=1898 & year<1903 & name=="cannon"
(5 real changes made)
.         replace period=2 if year>=1903 & year<=1911 & name=="cannon"
(9 real changes made)
.         replace period=3 if year>1911 & year<=1916 & name=="cannon"
(5 real changes made)
.         
.         replace period=1 if year>=1906 & year<1911 & name=="clark"
(5 real changes made)
.         replace period=2 if year>=1911 & year<=1919 & name=="clark"
(9 real changes made)
.         replace period=3 if year>1919 & year<=1924 & name=="clark"
(5 real changes made)
.         
.         replace period=1 if year>=1914 & year<1919 & name=="gillett"
(5 real changes made)
.         replace period=2 if year>=1919 & year<=1925 & name=="gillett"
(7 real changes made)
.         replace period=3 if year > 1925 & year<=1930 & name=="gillett"
(5 real changes made)
.         
.         replace period=1 if year>=1922 & year<1927 & name=="longworth"
(5 real changes made)
.         replace period=2 if year>=1927 & year<=1929 & name=="longworth"
(3 real changes made)
.         replace period=3 if year > 1929 & year<=1934 & name=="longworth"
(5 real changes made)
.         
.         replace period=1 if year>=1926 & year<1931 & name=="garner"
(5 real changes made)
.         replace period=2 if year>=1931 & year<=1933 & name=="garner"
(3 real changes made)
.         replace period=3 if year > 1933 & year<=1938 & name=="garner"
(5 real changes made)
.         
.         replace period=1 if year>=1928 & year<1933 & name=="rainey"
(5 real changes made)
.         replace period=2 if year>=1933 & year<=1934 & name=="rainey"
(2 real changes made)
.         replace period=3 if year > 1934 & year<=1939 & name=="rainey"
(5 real changes made)
.         
.         replace period=1 if year>=1930 & year<1935 & name=="byrns"
(5 real changes made)
.         replace period=2 if year>=1935 & year<=1936 & name=="byrns"
(2 real changes made)
.         replace period=3 if year > 1936 & year<=1941 & name=="byrns"
(5 real changes made)
.         
.         
.         replace period=1 if year>=1931 & year<1936 & name=="bankhead"
(5 real changes made)
.         replace period=2 if year>=1936 & year<=1940 & name=="bankhead"
(5 real changes made)
.         replace period=3 if year > 1940 & year<=1945 & name=="bankhead"
(5 real changes made)
.         
.         
.         replace period=1 if year>=1935 & year<1940 & name=="rayburn"
(5 real changes made)
.         replace period=2 if ([year>=1940 & year<=1947] | [year>=1949 & year<=1953] | [year>=1955 & year<=1961]) & name=="rayburn"
(20 real changes made)
.         replace period=3 if ([year>1947 & year<=1949] | [year>1953 & year<1955] | [year>1961 & year<1966]) & name=="rayburn"
(7 real changes made)
.         
.         
.         replace period=1 if year>=1884 & year<1889 & name=="martin"
(5 real changes made)
.         replace period=2 if ([year>=1947 & year<=1949] | [year>=1953 & year<=1955]) & name=="martin"
(6 real changes made)
.         replace period=3 if ([year>1949 & year<=1953] | [year>1955 & year<1960]) & name=="martin"
(8 real changes made)
.         
.         replace period=1 if year>=1957 & year<1962 & name=="mccormack"
(5 real changes made)
.         replace period=2 if year>=1962 & year<=1971 & name=="mccormack"
(10 real changes made)
.         replace period=3 if year > 1971 & year<=1976 & name=="mccormack"
(5 real changes made)
.         
.         replace period=1 if year>=1966 & year<1971 & name=="albert"
(5 real changes made)
.         replace period=2 if year>=1971 & year<=1977 & name=="albert"
(7 real changes made)
.         replace period=3 if year > 1977 & year<=1982 & name=="albert"
(5 real changes made)
.         
.         *minority leader
.         
.         replace period=4 if year>=1906 & year<1911 & name=="mann"
(5 real changes made)
.         replace period=5 if year>=1911 & year<=1919 & name=="mann"
(9 real changes made)
.         replace period=6 if year>1919 & year<=1924 & name=="mann"
(5 real changes made)
.         
.         replace period=4 if year>=1926 & year<1931 & name=="snell"
(5 real changes made)
.         replace period=5 if year>=1931 & year<=1939 & name=="snell"
(9 real changes made)
.         replace period=6 if year>1939 & year<=1944 & name=="snell"
(5 real changes made)
.         
.         replace period=4 if year>=1918 & year<1923 & name=="garrett"
(5 real changes made)
.         replace period=5 if year>=1923 & year<=1929 & name=="garrett"
(7 real changes made)
.         replace period=6 if year>1929 & year<=1934 & name=="garrett"
(5 real changes made)
.         
.         replace period=4 if year>=1884 & year<1889 & name=="halleck"
(5 real changes made)
.         replace period=5 if ([year>=1947 & year<=1949] | [year>=1953 & year<=1955]) & name=="halleck"
(6 real changes made)
.         replace period=6 if ([year>1949 & year<=1953] | [year>1955 & year<1960]) & name=="halleck"
(8 real changes made)
.         
.         
.         drop if period==.
(6,076 observations deleted)
.         sort year
.         bysort period name: egen rank=rank(_n)
.         drop year
.         reshape wide hits, i(rank name) j(period)
(note: j = 1 2 3 4 5 6)

Data                               long   ->   wide
-----------------------------------------------------------------------------
Number of obs.                      370   ->     163
Number of variables                   4   ->       8
j variable (6 values)            period   ->   (dropped)
xij variables:
                                   hits   ->   hits1 hits2 ... hits6
-----------------------------------------------------------------------------
.         
.         preserve
.         keep if hits1 != . & hits2 != . & hits3 != .
(91 observations deleted)
.         keep name hits1-hits3
.         drop if hits1 == 0 | hits2==0 | hits3==0
(27 observations deleted)
.         restore
.         
.         sum hits1

    Variable |        Obs        Mean    Std. Dev.       Min        Max
-------------+---------------------------------------------------------
       hits1 |         86    42.94186    60.99669          0        239
.         local m_before = r(mean)
.         local sd_before = r(sd)
.         local N_before =r(N)
.         
.         sum hits2

    Variable |        Obs        Mean    Std. Dev.       Min        Max
-------------+---------------------------------------------------------
       hits2 |        113    315.3097     502.409          0       2581
.         local m_during = r(mean)
.         local sd_during = r(sd)
.         local N_during =r(N)
.         
.         sum hits3

    Variable |        Obs        Mean    Std. Dev.       Min        Max
-------------+---------------------------------------------------------
       hits3 |         98    42.91837    88.32061          0        416
.         local m_after = r(mean)
.         local sd_after = r(sd)
.         local N_after =r(N)
.         
.         
.         local diff1 = `m_before' - `m_during'
.         ttest hits1==hits2, unpaired

Two-sample t test with equal variances
------------------------------------------------------------------------------
Variable |     Obs        Mean    Std. Err.   Std. Dev.   [95% Conf. Interval]
---------+--------------------------------------------------------------------
   hits1 |      86    42.94186    6.577443    60.99669    29.86414    56.01958
   hits2 |     113    315.3097    47.26266     502.409    221.6648    408.9546
---------+--------------------------------------------------------------------
combined |     199     197.603     28.5912    403.3285    141.2207    253.9854
---------+--------------------------------------------------------------------
    diff |           -272.3679    54.51136               -379.8686   -164.8672
------------------------------------------------------------------------------
    diff = mean(hits1) - mean(hits2)                              t =  -4.9965
Ho: diff = 0                                     degrees of freedom =      197

    Ha: diff < 0                 Ha: diff != 0                 Ha: diff > 0
 Pr(T < t) = 0.0000         Pr(|T| > |t|) = 0.0000          Pr(T > t) = 1.0000
.         local p1 = r(p)
.         local diff2 = `m_after' - `m_during'
.         ttest hits3==hits2, unpaired

Two-sample t test with equal variances
------------------------------------------------------------------------------
Variable |     Obs        Mean    Std. Err.   Std. Dev.   [95% Conf. Interval]
---------+--------------------------------------------------------------------
   hits3 |      98    42.91837    8.921728    88.32061    25.21121    60.62553
   hits2 |     113    315.3097    47.26266     502.409    221.6648    408.9546
---------+--------------------------------------------------------------------
combined |     211    188.7962    27.25757     395.939    135.0627    242.5297
---------+--------------------------------------------------------------------
    diff |           -272.3914    51.44198                -373.803   -170.9797
------------------------------------------------------------------------------
    diff = mean(hits3) - mean(hits2)                              t =  -5.2951
Ho: diff = 0                                     degrees of freedom =      209

    Ha: diff < 0                 Ha: diff != 0                 Ha: diff > 0
 Pr(T < t) = 0.0000         Pr(|T| > |t|) = 0.0000          Pr(T > t) = 1.0000
.         local p2 = r(p)
.         
.         **minority leaders
.         
.         sum hits4

    Variable |        Obs        Mean    Std. Dev.       Min        Max
-------------+---------------------------------------------------------
       hits4 |         20       23.75    31.95536          0        114
.         local lead_before = r(mean)
.         local lead_sd_before = r(sd)
.         local lead_N_before =r(N)
.         
.         sum hits5

    Variable |        Obs        Mean    Std. Dev.       Min        Max
-------------+---------------------------------------------------------
       hits5 |         30    139.6667    106.1428          6        401
.         local lead_during = r(mean)
.         local lead_sd_during = r(sd)
.         local lead_N_during =r(N)
.         
.         sum hits6

    Variable |        Obs        Mean    Std. Dev.       Min        Max
-------------+---------------------------------------------------------
       hits6 |         23    47.73913    75.72031          0        312
.         local lead_after = r(mean)
.         local lead_sd_after = r(sd)
.         local lead_N_after =r(N)
.         
.         local diff3 = `lead_before' - `lead_during'
.         ttest hits4==hits5, unpaired

Two-sample t test with equal variances
------------------------------------------------------------------------------
Variable |     Obs        Mean    Std. Err.   Std. Dev.   [95% Conf. Interval]
---------+--------------------------------------------------------------------
   hits4 |      20       23.75    7.145435    31.95536    8.794433    38.70557
   hits5 |      30    139.6667    19.37894    106.1428    100.0323     179.301
---------+--------------------------------------------------------------------
combined |      50        93.3    14.39054    101.7565    64.38113    122.2189
---------+--------------------------------------------------------------------
    diff |           -115.9167    24.51347               -165.2043   -66.62902
------------------------------------------------------------------------------
    diff = mean(hits4) - mean(hits5)                              t =  -4.7287
Ho: diff = 0                                     degrees of freedom =       48

    Ha: diff < 0                 Ha: diff != 0                 Ha: diff > 0
 Pr(T < t) = 0.0000         Pr(|T| > |t|) = 0.0000          Pr(T > t) = 1.0000
.         local p3 = r(p)
.         local diff4 = `lead_after' - `lead_during'
.         ttest hits6==hits5, unpaired

Two-sample t test with equal variances
------------------------------------------------------------------------------
Variable |     Obs        Mean    Std. Err.   Std. Dev.   [95% Conf. Interval]
---------+--------------------------------------------------------------------
   hits6 |      23    47.73913    15.78878    75.72031    14.99521    80.48305
   hits5 |      30    139.6667    19.37894    106.1428    100.0323     179.301
---------+--------------------------------------------------------------------
combined |      53    99.77358    14.29118    104.0413    71.09624    128.4509
---------+--------------------------------------------------------------------
    diff |           -91.92754    26.11625               -144.3581   -39.49698
------------------------------------------------------------------------------
    diff = mean(hits6) - mean(hits5)                              t =  -3.5199
Ho: diff = 0                                     degrees of freedom =       51

    Ha: diff < 0                 Ha: diff != 0                 Ha: diff > 0
 Pr(T < t) = 0.0005         Pr(|T| > |t|) = 0.0009          Pr(T > t) = 0.9995
.         local p4 = r(p)
. 
.         quietly {
---------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------
      name:  <unnamed>
       log:  /Users/pamelaban/Dropbox/newspapers_power/PSRM_replication/dataverse_upload/log_stata_files.log
  log type:  text
 opened on:  19 Apr 2017, 14:53:24

. 
. ********************************************************************************
. * Relative Coverage of City Offices Over Time
. * Table 2: Impact of Switch from Mayor-Countil to Council-Manager City Government
. * Table A.4: with linear, city-specific time trends 
. ********************************************************************************
. 
. if `make_mayors'==1 {
. 
.         use hits_citygov, clear
.         replace city = upper(city)
(129,686 real changes made)
.         replace city = "SAN BERNARDINO" if state == "CA" & strpos(city, "SAN BERNARDIN") > 0
(429 real changes made)
.         replace city = "WILMINGTON"     if state == "DE" & city=="WILMING"
(5 real changes made)
.         replace city = "NATCHITOCHES"   if state == "LA" & strpos(city, "NATCHITOCH") > 0
(6 real changes made)
.         drop if city == "UNITED STATES OF AMERICA"
(0 observations deleted)
.         drop if real(city) != .
(0 observations deleted)
.         drop if city == ""
(479 observations deleted)
.         drop if state == ""
(2,222 observations deleted)
.         collapse (sum) mayor city_manager city_council mayor_x city_manager_x city_council_x, by(state city year)
.         sort state city year
.         tempfile citygov
.         save `citygov', replace
(note: file /var/folders/61/8_4x2m71723620zdy7x618300000gn/T//S_03391.000003 not found)
file /var/folders/61/8_4x2m71723620zdy7x618300000gn/T//S_03391.000003 saved
. 
.         insheet using city_manager_dates.txt , names clear
(7 vars, 1,097 obs)
.         sort state city
.         tempfile tmp1
.         save `tmp1', replace
(note: file /var/folders/61/8_4x2m71723620zdy7x618300000gn/T//S_03391.000004 not found)
file /var/folders/61/8_4x2m71723620zdy7x618300000gn/T//S_03391.000004 saved
.         
.         use `citygov', replace
.         replace city = upper(city)
(0 real changes made)
.         drop if state == ""
(0 observations deleted)
.         drop if city == "OTTAWA" & state == "PA"
(0 observations deleted)
.         drop if city == "OTTAWA" & state == "TX"
(0 observations deleted)
.         drop if city == "WINNIPEG"
(0 observations deleted)
.         drop if city == "VANCOUVER"
(10 observations deleted)
.         replace city = "MOUNT VERNON" if city == "MT VERNON"
(38 real changes made)
.         sort state city
.         merge state city using `tmp1'
(note: you are using old merge syntax; see [D] merge for new syntax)
variables state city do not uniquely identify observations in the master data
(note: variable city was str22, now str28 to accommodate using data's values)
.         drop if year < 1876 | year > 1977
(5,665 observations deleted)
.         
.         sort state city year
.         
.         rename first_year x
.         replace x = subinstr(x, "?" ,"" ,.)
(330 real changes made)
.         gen first_year = real(x)
(21,451 missing values generated)
.         drop x
.         
.         save `tmp1', replace
file /var/folders/61/8_4x2m71723620zdy7x618300000gn/T//S_03391.000004 saved
.         
.         use `tmp1', replace
.         
.         egen total   = rsum(mayor   city_manager   city_council)
.         egen total_x = rsum(mayor_x city_manager_x city_council_x)
.         
.         foreach i of varlist mayor city_manager city_council {
  2.           gen r_`i'  = `i'/total if total >= 50
  3.         }
(22,602 missing values generated)
(22,602 missing values generated)
(22,602 missing values generated)
.         
.         foreach i of varlist mayor_x city_manager_x city_council_x {
  2.           gen r_`i'  = `i'/total_x if total_x >= 10
  3.         }
(24,684 missing values generated)
(24,684 missing values generated)
(24,684 missing values generated)
.         
.         gen city_manager_govt = (year >= first_year)
.         
.         egen x = count(r_mayor), by(state city)
.         
.         drop if x < 10
(17,824 observations deleted)
.         
.         keep if (good_case == "***** currently council-manager" & first_year != .) | (good_case == "***** currently mayor-council")
(5,439 observations deleted)
.         compress
  variable year was float now int
  variable first_year was float now int
  variable total was float now int
  variable total_x was float now int
  variable city_manager_govt was float now byte
  variable x was float now byte
  variable mayor was double now int
  variable city_manager was double now int
  variable city_council was double now int
  variable mayor_x was double now int
  variable city_manager_x was double now int
  variable city_council_x was double now int
  variable city was str28 now str16
  variable good_case was str87 now str31
  variable v6 was str6 now str1
  variable v7 was str6 now str1
  (841,216 bytes saved)
.         
.         keep if year >= 1890
(652 observations deleted)
.         tab year, gen(Y)

       year |      Freq.     Percent        Cum.
------------+-----------------------------------
       1890 |         59        1.00        1.00
       1891 |         68        1.15        2.15
       1892 |         68        1.15        3.29
       1893 |         65        1.10        4.39
       1894 |         69        1.17        5.56
       1895 |         68        1.15        6.71
       1896 |         65        1.10        7.80
       1897 |         67        1.13        8.94
       1898 |         66        1.11       10.05
       1899 |         66        1.11       11.17
       1900 |         58        0.98       12.15
       1901 |         63        1.06       13.21
       1902 |         62        1.05       14.26
       1903 |         59        1.00       15.25
       1904 |         60        1.01       16.27
       1905 |         61        1.03       17.30
       1906 |         63        1.06       18.36
       1907 |         64        1.08       19.44
       1908 |         65        1.10       20.54
       1909 |         69        1.17       21.71
       1910 |         62        1.05       22.75
       1911 |         68        1.15       23.90
       1912 |         68        1.15       25.05
       1913 |         67        1.13       26.18
       1914 |         68        1.15       27.33
       1915 |         70        1.18       28.51
       1916 |         72        1.22       29.73
       1917 |         71        1.20       30.93
       1918 |         70        1.18       32.11
       1919 |         69        1.17       33.28
       1920 |         72        1.22       34.49
       1921 |         74        1.25       35.74
       1922 |         68        1.15       36.89
       1923 |         54        0.91       37.80
       1924 |         51        0.86       38.67
       1925 |         51        0.86       39.53
       1926 |         54        0.91       40.44
       1927 |         52        0.88       41.32
       1928 |         53        0.90       42.21
       1929 |         60        1.01       43.23
       1930 |         63        1.06       44.29
       1931 |         62        1.05       45.34
       1932 |         62        1.05       46.39
       1933 |         64        1.08       47.47
       1934 |         63        1.06       48.53
       1935 |         65        1.10       49.63
       1936 |         66        1.11       50.74
       1937 |         68        1.15       51.89
       1938 |         63        1.06       52.96
       1939 |         65        1.10       54.05
       1940 |         64        1.08       55.14
       1941 |         66        1.11       56.25
       1942 |         68        1.15       57.40
       1943 |         65        1.10       58.50
       1944 |         65        1.10       59.59
       1945 |         66        1.11       60.71
       1946 |         65        1.10       61.81
       1947 |         64        1.08       62.89
       1948 |         66        1.11       64.00
       1949 |         62        1.05       65.05
       1950 |         66        1.11       66.17
       1951 |         63        1.06       67.23
       1952 |         65        1.10       68.33
       1953 |         70        1.18       69.51
       1954 |         75        1.27       70.78
       1955 |         73        1.23       72.01
       1956 |         78        1.32       73.33
       1957 |         73        1.23       74.56
       1958 |         76        1.28       75.84
       1959 |         73        1.23       77.08
       1960 |         71        1.20       78.28
       1961 |         75        1.27       79.54
       1962 |         76        1.28       80.83
       1963 |         81        1.37       82.20
       1964 |         78        1.32       83.51
       1965 |         79        1.33       84.85
       1966 |         74        1.25       86.10
       1967 |         76        1.28       87.38
       1968 |         75        1.27       88.65
       1969 |         72        1.22       89.86
       1970 |         73        1.23       91.10
       1971 |         75        1.27       92.36
       1972 |         73        1.23       93.60
       1973 |         75        1.27       94.86
       1974 |         76        1.28       96.15
       1975 |         81        1.37       97.52
       1976 |         75        1.27       98.78
       1977 |         72        1.22      100.00
------------+-----------------------------------
      Total |      5,920      100.00
.         
.         gen state_city = state + " " + city
.         
.         areg r_mayor          city_manager_govt Y*, a(state_city) cluster(state_city)
note: Y88 omitted because of collinearity

Linear regression, absorbing indicators         Number of obs     =      3,540
                                                F(  88,    120)   =      45.43
                                                Prob > F          =     0.0000
                                                R-squared         =     0.7621
                                                Adj R-squared     =     0.7472
                                                Root MSE          =     0.1011

                                (Std. Err. adjusted for 121 clusters in state_city)
-----------------------------------------------------------------------------------
                  |               Robust
          r_mayor |      Coef.   Std. Err.      t    P>|t|     [95% Conf. Interval]
------------------+----------------------------------------------------------------
city_manager_govt |  -.1831944   .0238659    -7.68   0.000    -.2304473   -.1359415
               Y1 |   .1598791   .0487469     3.28   0.001     .0633637    .2563946
               Y2 |   .1899831    .034097     5.57   0.000     .1224734    .2574929
               Y3 |   .1685355   .0385374     4.37   0.000     .0922342    .2448367
               Y4 |   .2128866   .0341901     6.23   0.000     .1451926    .2805807
               Y5 |   .1941713   .0340341     5.71   0.000     .1267862    .2615564
               Y6 |    .221392   .0338254     6.55   0.000       .15442     .288364
               Y7 |    .218376   .0349463     6.25   0.000     .1491847    .2875672
               Y8 |    .205559   .0303959     6.76   0.000     .1453772    .2657408
               Y9 |   .1948795   .0372853     5.23   0.000     .1210572    .2687018
              Y10 |   .2029339   .0333055     6.09   0.000     .1369913    .2688765
              Y11 |   .1903585   .0346322     5.50   0.000     .1217892    .2589279
              Y12 |   .2493604   .0308271     8.09   0.000     .1883249    .3103959
              Y13 |    .194198   .0333578     5.82   0.000      .128152    .2602441
              Y14 |    .199496   .0328256     6.08   0.000     .1345036    .2644883
              Y15 |   .1781655   .0331586     5.37   0.000     .1125139    .2438172
              Y16 |   .2240856   .0331417     6.76   0.000     .1584674    .2897038
              Y17 |   .1750351   .0338954     5.16   0.000     .1079246    .2421456
              Y18 |   .2144422   .0314005     6.83   0.000     .1522713    .2766131
              Y19 |   .1839455   .0320575     5.74   0.000     .1204738    .2474172
              Y20 |   .1955543   .0346692     5.64   0.000     .1269117    .2641969
              Y21 |   .2015282   .0327865     6.15   0.000     .1366133    .2664432
              Y22 |   .1962971   .0327781     5.99   0.000     .1313987    .2611955
              Y23 |   .2136317   .0327772     6.52   0.000      .148735    .2785283
              Y24 |   .1835659   .0272244     6.74   0.000     .1296635    .2374683
              Y25 |   .1466066    .029885     4.91   0.000     .0874365    .2057767
              Y26 |   .1156508   .0307752     3.76   0.000      .054718    .1765836
              Y27 |   .1328827    .028507     4.66   0.000     .0764407    .1893246
              Y28 |   .1167754   .0308931     3.78   0.000     .0556092    .1779416
              Y29 |   .0797662   .0340438     2.34   0.021     .0123618    .1471706
              Y30 |   .1267245   .0309932     4.09   0.000     .0653601    .1880889
              Y31 |   .0985386   .0317382     3.10   0.002     .0356991     .161378
              Y32 |   .1047372   .0345886     3.03   0.003     .0362541    .1732202
              Y33 |   .1011547   .0304757     3.32   0.001      .040815    .1614944
              Y34 |   .1086578   .0314999     3.45   0.001     .0462901    .1710255
              Y35 |   .1139301   .0320285     3.56   0.001     .0505159    .1773442
              Y36 |   .0978334   .0333633     2.93   0.004     .0317763    .1638905
              Y37 |   .0954894   .0330858     2.89   0.005     .0299819    .1609969
              Y38 |   .1030863   .0287277     3.59   0.000     .0462075    .1599651
              Y39 |   .0676348   .0358735     1.89   0.062    -.0033923    .1386618
              Y40 |   .0770107   .0320495     2.40   0.018     .0135548    .1404665
              Y41 |    .044958   .0298395     1.51   0.135    -.0141222    .1040382
              Y42 |   .1067446   .0263123     4.06   0.000      .054648    .1588411
              Y43 |   .1249916   .0245131     5.10   0.000     .0764573    .1735259
              Y44 |   .0977764    .023694     4.13   0.000     .0508639    .1446889
              Y45 |   .0959589    .027754     3.46   0.001     .0410078    .1509099
              Y46 |   .0857631   .0244634     3.51   0.001     .0373272    .1341989
              Y47 |   .0782075   .0247829     3.16   0.002      .029139     .127276
              Y48 |    .078153   .0256774     3.04   0.003     .0273136    .1289924
              Y49 |   .0768718   .0245187     3.14   0.002     .0283263    .1254172
              Y50 |   .0655158   .0243168     2.69   0.008     .0173702    .1136614
              Y51 |   .0500207   .0322211     1.55   0.123    -.0137749    .1138162
              Y52 |    .056856   .0281591     2.02   0.046     .0011029     .112609
              Y53 |   .0719767   .0292969     2.46   0.015     .0139709    .1299826
              Y54 |   .0461867   .0248789     1.86   0.066    -.0030718    .0954452
              Y55 |   .0180165   .0288882     0.62   0.534    -.0391801    .0752131
              Y56 |   .0376471   .0254207     1.48   0.141     -.012684    .0879783
              Y57 |   .0405195   .0241354     1.68   0.096    -.0072668    .0883058
              Y58 |   .0479711   .0234729     2.04   0.043     .0014964    .0944458
              Y59 |   .0307153   .0222704     1.38   0.170    -.0133784    .0748091
              Y60 |   .0548028   .0238488     2.30   0.023     .0075839    .1020217
              Y61 |    .039921   .0252344     1.58   0.116    -.0100413    .0898833
              Y62 |    .030515   .0236391     1.29   0.199    -.0162889    .0773188
              Y63 |  -.0068225   .0208493    -0.33   0.744    -.0481025    .0344576
              Y64 |   .0215402   .0223799     0.96   0.338    -.0227705    .0658509
              Y65 |   .0185571   .0234863     0.79   0.431     -.027944    .0650583
              Y66 |   .0193844   .0253121     0.77   0.445    -.0307319    .0695007
              Y67 |    .001184   .0216936     0.05   0.957    -.0417678    .0441357
              Y68 |   .0031212   .0204188     0.15   0.879    -.0373065     .043549
              Y69 |  -.0071949   .0233447    -0.31   0.758    -.0534158    .0390259
              Y70 |   .0410415   .0198081     2.07   0.040     .0018228    .0802602
              Y71 |   .0073837   .0195926     0.38   0.707    -.0314083    .0461758
              Y72 |   .0428434   .0212764     2.01   0.046     .0007176    .0849692
              Y73 |   .0124735   .0185536     0.67   0.503    -.0242614    .0492085
              Y74 |   .0444599   .0185662     2.39   0.018     .0077001    .0812196
              Y75 |  -.0031759   .0206462    -0.15   0.878    -.0440539    .0377021
              Y76 |   .0458351   .0181513     2.53   0.013     .0098968    .0817735
              Y77 |   .0247385   .0178929     1.38   0.169    -.0106882    .0601651
              Y78 |   .0484035   .0193747     2.50   0.014      .010043     .086764
              Y79 |   .0177875   .0183912     0.97   0.335    -.0186258    .0542007
              Y80 |   .0362657   .0159915     2.27   0.025     .0046036    .0679278
              Y81 |   .0050257   .0189027     0.27   0.791    -.0324004    .0424518
              Y82 |   .0370163   .0175803     2.11   0.037     .0022085    .0718242
              Y83 |  -.0024405   .0177635    -0.14   0.891    -.0376109      .03273
              Y84 |   .0015367    .017152     0.09   0.929     -.032423    .0354964
              Y85 |  -.0248803   .0153901    -1.62   0.109    -.0553516    .0055909
              Y86 |   .0161973   .0143228     1.13   0.260    -.0121608    .0445555
              Y87 |  -.0066693   .0161832    -0.41   0.681    -.0387109    .0253724
              Y88 |          0  (omitted)
            _cons |   .6033822   .0217963    27.68   0.000     .5602272    .6465373
------------------+----------------------------------------------------------------
       state_city |   absorbed                                     (121 categories)
.         local b_1 =  _b[city_manager_govt]
.         local s_1 = _se[city_manager_govt]
.         local n_1 =  e(N)
.         areg r_city_manager   city_manager_govt Y*, a(state_city) cluster(state_city)
note: Y88 omitted because of collinearity

Linear regression, absorbing indicators         Number of obs     =      3,540
                                                F(  88,    120)   =      46.80
                                                Prob > F          =     0.0000
                                                R-squared         =     0.7015
                                                Adj R-squared     =     0.6829
                                                Root MSE          =     0.0657

                                (Std. Err. adjusted for 121 clusters in state_city)
-----------------------------------------------------------------------------------
                  |               Robust
   r_city_manager |      Coef.   Std. Err.      t    P>|t|     [95% Conf. Interval]
------------------+----------------------------------------------------------------
city_manager_govt |    .178474   .0202245     8.82   0.000     .1384309     .218517
               Y1 |  -.0021186   .0164889    -0.13   0.898    -.0347655    .0305284
               Y2 |   .0049065   .0159084     0.31   0.758     -.026591    .0364039
               Y3 |   .0178627   .0194938     0.92   0.361    -.0207337    .0564591
               Y4 |    .001482   .0156297     0.09   0.925    -.0294637    .0324278
               Y5 |   .0031429   .0154368     0.20   0.839    -.0274209    .0337067
               Y6 |   .0050727   .0167343     0.30   0.762    -.0280601    .0382055
               Y7 |   .0031564   .0168797     0.19   0.852    -.0302643     .036577
               Y8 |   .0065245   .0165315     0.39   0.694    -.0262068    .0392558
               Y9 |    .010401   .0162822     0.64   0.524    -.0218367    .0426386
              Y10 |   .0062534   .0161841     0.39   0.700      -.02579    .0382967
              Y11 |   .0031349   .0151387     0.21   0.836    -.0268386    .0331084
              Y12 |   .0091352   .0159794     0.57   0.569    -.0225028    .0407732
              Y13 |   .0138125   .0158069     0.87   0.384    -.0174841    .0451091
              Y14 |   .0149377   .0162171     0.92   0.359    -.0171711    .0470464
              Y15 |   .0113641   .0164819     0.69   0.492     -.021269    .0439971
              Y16 |   .0075685   .0159267     0.48   0.636    -.0239653    .0391023
              Y17 |   .0091764    .015895     0.58   0.565    -.0222946    .0406475
              Y18 |   .0150701   .0159659     0.94   0.347    -.0165412    .0466814
              Y19 |    .009118   .0158231     0.58   0.566    -.0222107    .0404466
              Y20 |   .0076581    .015923     0.48   0.631    -.0238683    .0391845
              Y21 |      .0104   .0159606     0.65   0.516     -.021201    .0420009
              Y22 |   .0099193    .016084     0.62   0.539    -.0219258    .0417645
              Y23 |   .0169002   .0169381     1.00   0.320    -.0166361    .0504364
              Y24 |   .0145519    .016257     0.90   0.373    -.0176358    .0467397
              Y25 |   .0215406   .0161982     1.33   0.186    -.0105307     .053612
              Y26 |   .0242805   .0164694     1.47   0.143    -.0083279    .0568888
              Y27 |   .0223224   .0178238     1.25   0.213    -.0129675    .0576124
              Y28 |   .0495835   .0235163     2.11   0.037     .0030228    .0961442
              Y29 |   .0553505   .0301432     1.84   0.069    -.0043308    .1150318
              Y30 |   .0463968   .0247577     1.87   0.063    -.0026218    .0954153
              Y31 |   .0556396   .0242851     2.29   0.024     .0075567    .1037224
              Y32 |   .0443509   .0192928     2.30   0.023     .0061525    .0825493
              Y33 |   .0415312   .0218014     1.90   0.059     -.001634    .0846963
              Y34 |   .0274778   .0167305     1.64   0.103    -.0056475    .0606031
              Y35 |   .0094007   .0177491     0.53   0.597    -.0257413    .0445427
              Y36 |   .0203099   .0192951     1.05   0.295    -.0178931    .0585128
              Y37 |   .0264262   .0233065     1.13   0.259    -.0197191    .0725714
              Y38 |   .0469237   .0266268     1.76   0.081    -.0057956    .0996429
              Y39 |   .0401879   .0223206     1.80   0.074    -.0040053    .0843811
              Y40 |   .0303796   .0239575     1.27   0.207    -.0170545    .0778138
              Y41 |   .0375061   .0225061     1.67   0.098    -.0070544    .0820666
              Y42 |   .0309761   .0211049     1.47   0.145    -.0108101    .0727623
              Y43 |   .0215988   .0175814     1.23   0.222    -.0132111    .0564087
              Y44 |   .0297619   .0198899     1.50   0.137    -.0096188    .0691425
              Y45 |   .0329792    .023686     1.39   0.166    -.0139175    .0798758
              Y46 |    .038685   .0259813     1.49   0.139    -.0127561    .0901261
              Y47 |   .0372283   .0218981     1.70   0.092    -.0061284     .080585
              Y48 |   .0406847   .0199093     2.04   0.043     .0012656    .0801038
              Y49 |   .0607499    .020486     2.97   0.004      .020189    .1013108
              Y50 |   .0549298   .0204973     2.68   0.008     .0143466    .0955131
              Y51 |   .0642433   .0263722     2.44   0.016     .0120283    .1164583
              Y52 |   .0572083   .0247326     2.31   0.022     .0082396    .1061771
              Y53 |   .0606119   .0246704     2.46   0.015     .0117662    .1094577
              Y54 |   .0602936   .0202269     2.98   0.003     .0202456    .1003415
              Y55 |   .0679956   .0236121     2.88   0.005     .0212452    .1147459
              Y56 |   .0586171    .023682     2.48   0.015     .0117284    .1055057
              Y57 |   .0658487   .0191324     3.44   0.001     .0279679    .1037295
              Y58 |   .0591879   .0173338     3.41   0.001     .0248682    .0935077
              Y59 |   .0678675   .0170463     3.98   0.000     .0341171     .101618
              Y60 |   .0345855   .0140443     2.46   0.015     .0067787    .0623923
              Y61 |   .0467314   .0150716     3.10   0.002     .0168907    .0765721
              Y62 |   .0361538   .0210202     1.72   0.088    -.0054648    .0777724
              Y63 |   .0796266   .0186941     4.26   0.000     .0426135    .1166397
              Y64 |   .0401019    .014843     2.70   0.008     .0107138    .0694899
              Y65 |   .0325004   .0143023     2.27   0.025     .0041828     .060818
              Y66 |   .0424439   .0157846     2.69   0.008     .0111914    .0736964
              Y67 |   .0648637   .0167184     3.88   0.000     .0317625    .0979649
              Y68 |   .0462451   .0167143     2.77   0.007     .0131519    .0793382
              Y69 |   .0530597   .0167695     3.16   0.002     .0198572    .0862621
              Y70 |   .0350494   .0124381     2.82   0.006     .0104229     .059676
              Y71 |   .0439212   .0151515     2.90   0.004     .0139222    .0739201
              Y72 |   .0311241   .0120828     2.58   0.011      .007201    .0550471
              Y73 |    .027026   .0120638     2.24   0.027     .0031405    .0509115
              Y74 |   .0159716   .0110112     1.45   0.150    -.0058298     .037773
              Y75 |   .0300271    .012935     2.32   0.022     .0044166    .0556376
              Y76 |  -.0068144   .0116484    -0.59   0.560    -.0298774    .0162485
              Y77 |   .0033801   .0116369     0.29   0.772    -.0196601    .0264204
              Y78 |   .0092289    .010974     0.84   0.402     -.012499    .0309567
              Y79 |   .0050939   .0109842     0.46   0.644     -.016654    .0268418
              Y80 |   .0007643   .0104227     0.07   0.942    -.0198719    .0214005
              Y81 |   .0100522   .0126089     0.80   0.427    -.0149125    .0350169
              Y82 |   .0040285   .0104565     0.39   0.701    -.0166747    .0247317
              Y83 |   .0239731   .0126198     1.90   0.060    -.0010131    .0489593
              Y84 |  -.0006182    .011018    -0.06   0.955    -.0224331    .0211968
              Y85 |   .0072987   .0106194     0.69   0.493     -.013727    .0283245
              Y86 |   .0020374   .0088261     0.23   0.818    -.0154377    .0195124
              Y87 |   .0112193   .0088469     1.27   0.207     -.006297    .0287355
              Y88 |          0  (omitted)
            _cons |  -.0038838   .0161697    -0.24   0.811    -.0358988    .0281311
------------------+----------------------------------------------------------------
       state_city |   absorbed                                     (121 categories)
.         local b_2 =  _b[city_manager_govt]
.         local s_2 = _se[city_manager_govt]
.         local n_2 =  e(N)
.         areg r_city_council   city_manager_govt Y*, a(state_city) cluster(state_city)
note: Y88 omitted because of collinearity

Linear regression, absorbing indicators         Number of obs     =      3,540
                                                F(  88,    120)   =      14.90
                                                Prob > F          =     0.0000
                                                R-squared         =     0.6404
                                                Adj R-squared     =     0.6179
                                                Root MSE          =     0.1002

                                (Std. Err. adjusted for 121 clusters in state_city)
-----------------------------------------------------------------------------------
                  |               Robust
   r_city_council |      Coef.   Std. Err.      t    P>|t|     [95% Conf. Interval]
------------------+----------------------------------------------------------------
city_manager_govt |   .0047204    .026843     0.18   0.861    -.0484268    .0578676
               Y1 |  -.1577606   .0522337    -3.02   0.003    -.2611797   -.0543414
               Y2 |  -.1948896    .035889    -5.43   0.000    -.2659474   -.1238318
               Y3 |  -.1863982   .0393478    -4.74   0.000     -.264304   -.1084923
               Y4 |  -.2143686   .0348725    -6.15   0.000    -.2834139   -.1453234
               Y5 |  -.1973142   .0368415    -5.36   0.000    -.2702577   -.1243707
               Y6 |  -.2264647   .0363992    -6.22   0.000    -.2985325   -.1543968
               Y7 |  -.2215323   .0377181    -5.87   0.000    -.2962115   -.1468531
               Y8 |  -.2120835   .0320641    -6.61   0.000    -.2755682   -.1485988
               Y9 |  -.2052805   .0371118    -5.53   0.000    -.2787591   -.1318018
              Y10 |  -.2091873   .0350732    -5.96   0.000    -.2786297   -.1397448
              Y11 |  -.1934934   .0357058    -5.42   0.000    -.2641885   -.1227983
              Y12 |  -.2584956   .0324414    -7.97   0.000    -.3227273   -.1942638
              Y13 |  -.2080105   .0339956    -6.12   0.000    -.2753194   -.1407017
              Y14 |  -.2144336   .0327289    -6.55   0.000    -.2792345   -.1496328
              Y15 |  -.1895296   .0352882    -5.37   0.000    -.2593979   -.1196614
              Y16 |  -.2316541   .0349818    -6.62   0.000    -.3009157   -.1623925
              Y17 |  -.1842116   .0350095    -5.26   0.000    -.2535278   -.1148953
              Y18 |  -.2295123   .0330068    -6.95   0.000    -.2948634   -.1641612
              Y19 |  -.1930634   .0350646    -5.51   0.000     -.262489   -.1236379
              Y20 |  -.2032124   .0380031    -5.35   0.000    -.2784558    -.127969
              Y21 |  -.2119282   .0358419    -5.91   0.000    -.2828927   -.1409637
              Y22 |  -.2062164   .0334531    -6.16   0.000    -.2724512   -.1399817
              Y23 |  -.2305318   .0343255    -6.72   0.000     -.298494   -.1625697
              Y24 |  -.1981179   .0286667    -6.91   0.000     -.254876   -.1413597
              Y25 |  -.1681472   .0326104    -5.16   0.000    -.2327135   -.1035809
              Y26 |  -.1399312   .0325116    -4.30   0.000     -.204302   -.0755605
              Y27 |  -.1552051   .0300039    -5.17   0.000    -.2146108   -.0957994
              Y28 |  -.1663589   .0327233    -5.08   0.000    -.2311487   -.1015691
              Y29 |  -.1351167   .0362123    -3.73   0.000    -.2068145   -.0634188
              Y30 |  -.1731213   .0316715    -5.47   0.000    -.2358286   -.1104139
              Y31 |  -.1541782   .0319465    -4.83   0.000    -.2174299   -.0909264
              Y32 |   -.149088   .0339039    -4.40   0.000    -.2162153   -.0819608
              Y33 |  -.1426858   .0298606    -4.78   0.000    -.2018077   -.0835639
              Y34 |  -.1361356   .0320094    -4.25   0.000     -.199512   -.0727592
              Y35 |  -.1233308   .0331034    -3.73   0.000    -.1888733   -.0577882
              Y36 |  -.1181433   .0378264    -3.12   0.002     -.193037   -.0432496
              Y37 |  -.1219156   .0362484    -3.36   0.001    -.1936849   -.0501464
              Y38 |    -.15001   .0325201    -4.61   0.000    -.2143975   -.0856225
              Y39 |  -.1078227   .0383554    -2.81   0.006    -.1837637   -.0318817
              Y40 |  -.1073903   .0318691    -3.37   0.001     -.170489   -.0442916
              Y41 |  -.0824641   .0314971    -2.62   0.010    -.1448261   -.0201021
              Y42 |  -.1377206   .0257778    -5.34   0.000    -.1887589   -.0866823
              Y43 |  -.1465904   .0251063    -5.84   0.000    -.1962991   -.0968816
              Y44 |  -.1275383    .024069    -5.30   0.000    -.1751931   -.0798834
              Y45 |   -.128938   .0246676    -5.23   0.000    -.1777781   -.0800979
              Y46 |  -.1244481   .0240633    -5.17   0.000    -.1720917   -.0768045
              Y47 |  -.1154358   .0224533    -5.14   0.000    -.1598919   -.0709798
              Y48 |  -.1188376   .0244269    -4.87   0.000    -.1672013    -.070474
              Y49 |  -.1376217   .0249675    -5.51   0.000    -.1870556   -.0881877
              Y50 |  -.1204456   .0246173    -4.89   0.000    -.1691862    -.071705
              Y51 |   -.114264   .0300045    -3.81   0.000    -.1736709    -.054857
              Y52 |  -.1140643   .0246482    -4.63   0.000     -.162866   -.0652626
              Y53 |  -.1325886   .0241746    -5.48   0.000    -.1804528   -.0847245
              Y54 |  -.1064803   .0226006    -4.71   0.000    -.1512278   -.0617327
              Y55 |  -.0860121   .0295443    -2.91   0.004    -.1445077   -.0275164
              Y56 |  -.0962642   .0273668    -3.52   0.001    -.1504486   -.0420798
              Y57 |  -.1063682   .0229543    -4.63   0.000    -.1518162   -.0609203
              Y58 |   -.107159   .0238335    -4.50   0.000    -.1543477   -.0599703
              Y59 |  -.0985829   .0240752    -4.09   0.000      -.14625   -.0509158
              Y60 |  -.0893883   .0240716    -3.71   0.000    -.1370484   -.0417283
              Y61 |  -.0866524   .0250759    -3.46   0.001     -.136301   -.0370038
              Y62 |  -.0666688   .0237027    -2.81   0.006    -.1135985    -.019739
              Y63 |  -.0728041   .0214995    -3.39   0.001    -.1153716   -.0302367
              Y64 |   -.061642   .0202413    -3.05   0.003    -.1017183   -.0215657
              Y65 |  -.0510575   .0246419    -2.07   0.040    -.0998468   -.0022682
              Y66 |  -.0618283   .0229737    -2.69   0.008    -.1073146    -.016342
              Y67 |  -.0660477   .0196226    -3.37   0.001    -.1048991   -.0271963
              Y68 |  -.0493663   .0201647    -2.45   0.016    -.0892911   -.0094415
              Y69 |  -.0458648   .0216682    -2.12   0.036    -.0887662   -.0029633
              Y70 |  -.0760909   .0182175    -4.18   0.000    -.1121603   -.0400216
              Y71 |  -.0513049   .0191978    -2.67   0.009    -.0893152   -.0132946
              Y72 |  -.0739675   .0207117    -3.57   0.001    -.1149753   -.0329597
              Y73 |  -.0394996   .0190061    -2.08   0.040    -.0771303   -.0018688
              Y74 |  -.0604315    .018306    -3.30   0.001     -.096676   -.0241869
              Y75 |  -.0268512   .0201211    -1.33   0.185    -.0666897    .0129872
              Y76 |  -.0390207   .0177263    -2.20   0.030    -.0741176   -.0039238
              Y77 |  -.0281186   .0175257    -1.60   0.111    -.0628183     .006581
              Y78 |  -.0576324   .0170058    -3.39   0.001    -.0913027    -.023962
              Y79 |  -.0228814   .0190958    -1.20   0.233    -.0606898     .014927
              Y80 |    -.03703   .0169737    -2.18   0.031    -.0706367   -.0034234
              Y81 |  -.0150779   .0197849    -0.76   0.448    -.0542508    .0240949
              Y82 |  -.0410448    .018358    -2.24   0.027    -.0773924   -.0046973
              Y83 |  -.0215326   .0178448    -1.21   0.230    -.0568641    .0137988
              Y84 |  -.0009185   .0166596    -0.06   0.956    -.0339033    .0320663
              Y85 |   .0175816    .015101     1.16   0.247    -.0123173    .0474805
              Y86 |  -.0182347   .0142624    -1.28   0.204    -.0464732    .0100039
              Y87 |    -.00455   .0152246    -0.30   0.766    -.0346936    .0255936
              Y88 |          0  (omitted)
            _cons |   .4005016   .0228389    17.54   0.000     .3552821    .4457211
------------------+----------------------------------------------------------------
       state_city |   absorbed                                     (121 categories)
.         
.         areg r_mayor_x        city_manager_govt Y*, a(state_city) cluster(state_city)
note: Y88 omitted because of collinearity

Linear regression, absorbing indicators         Number of obs     =      2,376
                                                F(  88,    117)   =      33.69
                                                Prob > F          =     0.0000
                                                R-squared         =     0.6965
                                                Adj R-squared     =     0.6678
                                                Root MSE          =     0.1719

                                (Std. Err. adjusted for 118 clusters in state_city)
-----------------------------------------------------------------------------------
                  |               Robust
        r_mayor_x |      Coef.   Std. Err.      t    P>|t|     [95% Conf. Interval]
------------------+----------------------------------------------------------------
city_manager_govt |  -.2503144   .0396004    -6.32   0.000     -.328741   -.1718879
               Y1 |   .2405623   .0862372     2.79   0.006      .069774    .4113507
               Y2 |   .2773904   .0657923     4.22   0.000     .1470922    .4076887
               Y3 |   .2127631   .0710892     2.99   0.003     .0719747    .3535515
               Y4 |   .3268792   .0566327     5.77   0.000     .2147212    .4390373
               Y5 |   .2315189   .0685948     3.38   0.001     .0956705    .3673672
               Y6 |   .2292825   .0748074     3.06   0.003     .0811305    .3774346
               Y7 |   .3130032   .0591727     5.29   0.000     .1958147    .4301918
               Y8 |   .2841598   .0598193     4.75   0.000     .1656909    .4026287
               Y9 |     .25246   .0681927     3.70   0.000     .1174079    .3875121
              Y10 |   .2255405   .0676964     3.33   0.001     .0914713    .3596097
              Y11 |   .3048324   .0609627     5.00   0.000      .184099    .4255658
              Y12 |   .2478337   .0665844     3.72   0.000     .1159668    .3797006
              Y13 |   .2918547   .0708542     4.12   0.000     .1515317    .4321776
              Y14 |    .272889   .0556487     4.90   0.000     .1626798    .3830983
              Y15 |   .2196017   .0665636     3.30   0.001     .0877759    .3514275
              Y16 |   .3120326   .0632439     4.93   0.000     .1867813    .4372838
              Y17 |   .1714581   .0595695     2.88   0.005     .0534838    .2894323
              Y18 |   .2380638   .0651177     3.66   0.000     .1091016     .367026
              Y19 |   .2793134   .0653144     4.28   0.000     .1499615    .4086652
              Y20 |   .2520128   .0651786     3.87   0.000     .1229299    .3810956
              Y21 |   .2559347   .0593176     4.31   0.000     .1384593      .37341
              Y22 |    .283081   .0604088     4.69   0.000     .1634445    .4027176
              Y23 |   .2541892   .0627487     4.05   0.000     .1299187    .3784597
              Y24 |   .2279972   .0545465     4.18   0.000     .1199708    .3360236
              Y25 |   .1763901    .067994     2.59   0.011     .0417316    .3110486
              Y26 |   .2300246   .0595941     3.86   0.000     .1120016    .3480476
              Y27 |    .215931   .0550386     3.92   0.000     .1069299    .3249321
              Y28 |   .1846702   .0715638     2.58   0.011     .0429418    .3263986
              Y29 |    .170742   .0667492     2.56   0.012     .0385487    .3029353
              Y30 |   .2551252   .0626388     4.07   0.000     .1310722    .3791781
              Y31 |   .1604248   .0738998     2.17   0.032     .0140702    .3067794
              Y32 |   .2297213   .0647134     3.55   0.001     .1015598    .3578828
              Y33 |   .2096359   .0653164     3.21   0.002     .0802803    .3389916
              Y34 |   .2451515    .067492     3.63   0.000     .1114872    .3788158
              Y35 |   .2423318   .0693957     3.49   0.001     .1048974    .3797663
              Y36 |   .1900841   .0819325     2.32   0.022     .0278211     .352347
              Y37 |   .2069613   .0688384     3.01   0.003     .0706304    .3432921
              Y38 |   .2227093   .0601529     3.70   0.000     .1035796    .3418391
              Y39 |    .070161   .1017476     0.69   0.492    -.1313449    .2716668
              Y40 |   .1930976   .0614395     3.14   0.002     .0714199    .3147752
              Y41 |   .1143141   .0544102     2.10   0.038     .0065576    .2220706
              Y42 |    .158578   .0591867     2.68   0.008     .0413619    .2757941
              Y43 |   .2013497   .0620361     3.25   0.002     .0784905     .324209
              Y44 |   .1829507   .0548306     3.34   0.001     .0743617    .2915398
              Y45 |    .240483   .0476609     5.05   0.000     .1460931     .334873
              Y46 |   .1682721    .047735     3.53   0.001     .0737355    .2628087
              Y47 |   .2123233   .0551574     3.85   0.000      .103087    .3215597
              Y48 |   .1402792     .05183     2.71   0.008     .0376326    .2429257
              Y49 |    .137348   .0500241     2.75   0.007      .038278    .2364181
              Y50 |   .1709309   .0562303     3.04   0.003     .0595697    .2822921
              Y51 |   .2474045   .0531394     4.66   0.000     .1421646    .3526444
              Y52 |   .1615689   .0544616     2.97   0.004     .0537105    .2694272
              Y53 |   .1810297   .0514915     3.52   0.001     .0790536    .2830058
              Y54 |   .1985422   .0501988     3.96   0.000     .0991262    .2979582
              Y55 |   .0980193   .0617093     1.59   0.115    -.0241928    .2202314
              Y56 |   .1425015   .0445349     3.20   0.002     .0543025    .2307005
              Y57 |    .114013   .0441658     2.58   0.011      .026545     .201481
              Y58 |   .1079067   .0491771     2.19   0.030     .0105139    .2052994
              Y59 |   .1339719    .043779     3.06   0.003     .0472698     .220674
              Y60 |   .1406507   .0445787     3.16   0.002     .0523649    .2289364
              Y61 |   .1221606   .0488585     2.50   0.014      .025399    .2189223
              Y62 |   .1670897   .0464074     3.60   0.000     .0751824    .2589971
              Y63 |   .1042629   .0436663     2.39   0.019      .017784    .1907418
              Y64 |   .1043734   .0426935     2.44   0.016     .0198212    .1889257
              Y65 |   .1129982   .0412341     2.74   0.007     .0313362    .1946601
              Y66 |   .0988737   .0441463     2.24   0.027     .0114442    .1863032
              Y67 |   .0874955   .0439783     1.99   0.049     .0003988    .1745922
              Y68 |   .0705281   .0397389     1.77   0.079    -.0081727     .149229
              Y69 |   .0829608   .0492608     1.68   0.095    -.0145976    .1805191
              Y70 |   .0911253    .040487     2.25   0.026      .010943    .1713076
              Y71 |   .0641578   .0404572     1.59   0.115    -.0159655    .1442811
              Y72 |   .1153214   .0417902     2.76   0.007      .032558    .1980848
              Y73 |   .0791125   .0368376     2.15   0.034     .0061575    .1520674
              Y74 |   .1130156   .0353993     3.19   0.002     .0429091    .1831221
              Y75 |   .0506278   .0375176     1.35   0.180    -.0236738    .1249295
              Y76 |   .0884223   .0335703     2.63   0.010      .021938    .1549065
              Y77 |   .0330678   .0404967     0.82   0.416    -.0471339    .1132695
              Y78 |   .0733361   .0359709     2.04   0.044     .0020976    .1445746
              Y79 |   .0472791   .0340665     1.39   0.168    -.0201877    .1147459
              Y80 |     .02732   .0326535     0.84   0.404    -.0373485    .0919885
              Y81 |   .0135711   .0331211     0.41   0.683    -.0520235    .0791657
              Y82 |   .0749963   .0362539     2.07   0.041     .0031974    .1467952
              Y83 |  -.0102909   .0318549    -0.32   0.747     -.073378    .0527961
              Y84 |   .0489002   .0357336     1.37   0.174    -.0218683    .1196688
              Y85 |  -.0119804   .0307417    -0.39   0.697    -.0728627    .0489018
              Y86 |   .0268192   .0334408     0.80   0.424    -.0394087     .093047
              Y87 |  -.0052782   .0265146    -0.20   0.843    -.0577889    .0472325
              Y88 |          0  (omitted)
            _cons |   .5129889    .038971    13.16   0.000     .4358088     .590169
------------------+----------------------------------------------------------------
       state_city |   absorbed                                     (118 categories)
.         local b_3 =  _b[city_manager_govt]
.         local s_3 = _se[city_manager_govt]
.         local n_3 =  e(N)
.         areg r_city_manager_x city_manager_govt Y*, a(state_city) cluster(state_city)
note: Y88 omitted because of collinearity

Linear regression, absorbing indicators         Number of obs     =      2,376
                                                F(  88,    117)   =      34.00
                                                Prob > F          =     0.0000
                                                R-squared         =     0.6840
                                                Adj R-squared     =     0.6541
                                                Root MSE          =     0.1143

                                (Std. Err. adjusted for 118 clusters in state_city)
-----------------------------------------------------------------------------------
                  |               Robust
 r_city_manager_x |      Coef.   Std. Err.      t    P>|t|     [95% Conf. Interval]
------------------+----------------------------------------------------------------
city_manager_govt |   .2850221   .0287042     9.93   0.000     .2281748    .3418693
               Y1 |    .041934   .0294188     1.43   0.157    -.0163283    .1001964
               Y2 |    .035106    .038017     0.92   0.358    -.0401847    .1103967
               Y3 |   .0412197   .0317895     1.30   0.197    -.0217378    .1041771
               Y4 |   .0465104    .029359     1.58   0.116    -.0116336    .1046543
               Y5 |   .0071643   .0370301     0.19   0.847     -.066172    .0805006
               Y6 |   .0412561    .036823     1.12   0.265      -.03167    .1141821
               Y7 |    .040591   .0348445     1.16   0.246    -.0284167    .1095987
               Y8 |   .0461861   .0324634     1.42   0.157     -.018106    .1104781
               Y9 |   .0264695    .035421     0.75   0.456    -.0436799    .0966189
              Y10 |   .0590305   .0308158     1.92   0.058    -.0019985    .1200596
              Y11 |   .0379552   .0285718     1.33   0.187    -.0186298    .0945402
              Y12 |   .0411771   .0394106     1.04   0.298    -.0368734    .1192277
              Y13 |   .0562323    .031028     1.81   0.073     -.005217    .1176816
              Y14 |   .0741156   .0317984     2.33   0.021     .0111404    .1370907
              Y15 |   .0427412   .0326074     1.31   0.192     -.021836    .1073184
              Y16 |   .0582587   .0316678     1.84   0.068    -.0044577    .1209751
              Y17 |    .051536   .0310197     1.66   0.099    -.0098968    .1129689
              Y18 |   .0574862    .030269     1.90   0.060      -.00246    .1174325
              Y19 |   .0589193   .0295488     1.99   0.048     .0003995    .1174391
              Y20 |   .0675449   .0301498     2.24   0.027     .0078348    .1272551
              Y21 |   .0556531   .0293336     1.90   0.060    -.0024405    .1137467
              Y22 |   .0654597   .0309842     2.11   0.037     .0040971    .1268223
              Y23 |   .0768631   .0440706     1.74   0.084    -.0104163    .1641426
              Y24 |   .0706703   .0316189     2.24   0.027     .0080507    .1332898
              Y25 |   .0774758   .0325383     2.38   0.019     .0130354    .1419161
              Y26 |   .0770384   .0316181     2.44   0.016     .0144203    .1396565
              Y27 |   .0825211   .0333646     2.47   0.015     .0164442    .1485981
              Y28 |    .098239   .0450445     2.18   0.031     .0090306    .1874474
              Y29 |   .0985498    .047689     2.07   0.041     .0041042    .1929954
              Y30 |   .0838883   .0447371     1.88   0.063    -.0047111    .1724877
              Y31 |   .0767598    .047731     1.61   0.110    -.0177689    .1712885
              Y32 |   .0676435   .0411391     1.64   0.103    -.0138304    .1491173
              Y33 |   .0575372    .036001     1.60   0.113    -.0137609    .1288352
              Y34 |   .0428542   .0335958     1.28   0.205    -.0236804    .1093888
              Y35 |   .0490211   .0474829     1.03   0.304    -.0450162    .1430584
              Y36 |   .1024294   .0567015     1.81   0.073    -.0098649    .2147238
              Y37 |   .0685072   .0345434     1.98   0.050     .0000957    .1369187
              Y38 |   .0862294   .0430624     2.00   0.048     .0009466    .1715122
              Y39 |   .0919371   .0455364     2.02   0.046     .0017547    .1821196
              Y40 |   .0710816   .0578118     1.23   0.221    -.0434116    .1855748
              Y41 |   .0932562   .0540168     1.73   0.087    -.0137212    .2002335
              Y42 |   .1163317   .0541076     2.15   0.034     .0091743     .223489
              Y43 |   .0584109    .042923     1.36   0.176    -.0265958    .1434176
              Y44 |   .0363839   .0366594     0.99   0.323     -.036218    .1089859
              Y45 |   .0411806   .0409821     1.00   0.317    -.0399824    .1223436
              Y46 |   .1054277   .0425086     2.48   0.015     .0212417    .1896137
              Y47 |   .0830114   .0425551     1.95   0.053    -.0012667    .1672895
              Y48 |   .0988807   .0326572     3.03   0.003     .0342047    .1635566
              Y49 |   .1320811   .0430577     3.07   0.003     .0468075    .2173547
              Y50 |   .1091402   .0530309     2.06   0.042     .0041153     .214165
              Y51 |   .0934224   .0382687     2.44   0.016     .0176332    .1692117
              Y52 |   .0434488    .036518     1.19   0.237    -.0288733    .1157708
              Y53 |   .0624021   .0382095     1.63   0.105    -.0132698     .138074
              Y54 |   .0879137   .0458718     1.92   0.058    -.0029329    .1787603
              Y55 |   .1107016   .0407208     2.72   0.008     .0300562    .1913471
              Y56 |   .1037223   .0355785     2.92   0.004     .0332609    .1741837
              Y57 |   .1025442   .0397031     2.58   0.011     .0239142    .1811741
              Y58 |     .13033   .0315955     4.12   0.000     .0677568    .1929033
              Y59 |   .1164471   .0406988     2.86   0.005     .0358452     .197049
              Y60 |   .0751142   .0333309     2.25   0.026     .0091041    .1411243
              Y61 |   .1506966   .0452938     3.33   0.001     .0609945    .2403987
              Y62 |   .0298675   .0394114     0.76   0.450    -.0481847    .1079197
              Y63 |   .1298278    .042777     3.03   0.003     .0451102    .2145454
              Y64 |   .1008186   .0409571     2.46   0.015     .0197053     .181932
              Y65 |    .081854   .0399817     2.05   0.043     .0026722    .1610357
              Y66 |   .1006803   .0404631     2.49   0.014     .0205454    .1808153
              Y67 |   .0946457   .0378949     2.50   0.014     .0195969    .1696945
              Y68 |   .1066097   .0328939     3.24   0.002     .0414651    .1717542
              Y69 |   .0811516   .0304079     2.67   0.009     .0209304    .1413729
              Y70 |    .074289     .03083     2.41   0.018     .0132318    .1353461
              Y71 |   .0612102   .0318505     1.92   0.057     -.001868    .1242884
              Y72 |   .0559692    .027245     2.05   0.042     .0020119    .1099265
              Y73 |   .0406547   .0281069     1.45   0.151    -.0150095    .0963188
              Y74 |   .0352788   .0253964     1.39   0.167    -.0150175     .085575
              Y75 |    .060423   .0262599     2.30   0.023     .0084167    .1124294
              Y76 |   .0061435   .0252928     0.24   0.809    -.0439474    .0562345
              Y77 |   .0576424   .0302034     1.91   0.059    -.0021739    .1174587
              Y78 |   .0213488   .0253068     0.84   0.401      -.02877    .0714675
              Y79 |   .0383317   .0261498     1.47   0.145    -.0134566      .09012
              Y80 |   .0065099   .0281607     0.23   0.818     -.049261    .0622807
              Y81 |   .0188225    .024675     0.76   0.447    -.0300451      .06769
              Y82 |   .0122895    .026825     0.46   0.648    -.0408361    .0654151
              Y83 |   .0494578   .0258209     1.92   0.058    -.0016792    .1005949
              Y84 |  -.0207139   .0245592    -0.84   0.401    -.0693521    .0279242
              Y85 |  -.0006737   .0225217    -0.03   0.976    -.0452766    .0439293
              Y86 |   .0023698   .0244767     0.10   0.923     -.046105    .0508445
              Y87 |   .0252634   .0185691     1.36   0.176    -.0115117    .0620386
              Y88 |          0  (omitted)
            _cons |  -.0467883   .0302919    -1.54   0.125    -.1067798    .0132032
------------------+----------------------------------------------------------------
       state_city |   absorbed                                     (118 categories)
.         local b_4 =  _b[city_manager_govt]
.         local s_4 = _se[city_manager_govt]
.         local n_4 =  e(N)
.         areg r_city_council_x city_manager_govt Y*, a(state_city) cluster(state_city)
note: Y88 omitted because of collinearity

Linear regression, absorbing indicators         Number of obs     =      2,376
                                                F(  88,    117)   =      15.11
                                                Prob > F          =     0.0000
                                                R-squared         =     0.5716
                                                Adj R-squared     =     0.5311
                                                Root MSE          =     0.1724

                                (Std. Err. adjusted for 118 clusters in state_city)
-----------------------------------------------------------------------------------
                  |               Robust
 r_city_council_x |      Coef.   Std. Err.      t    P>|t|     [95% Conf. Interval]
------------------+----------------------------------------------------------------
city_manager_govt |  -.0347077   .0431548    -0.80   0.423    -.1201736    .0507582
               Y1 |  -.2824964   .0863769    -3.27   0.001    -.4535613   -.1114315
               Y2 |  -.3124964   .0803154    -3.89   0.000    -.4715569   -.1534359
               Y3 |  -.2539827   .0780913    -3.25   0.001    -.4086385    -.099327
               Y4 |  -.3733896   .0579799    -6.44   0.000    -.4882158   -.2585634
               Y5 |  -.2386832   .0741059    -3.22   0.002    -.3854461   -.0919202
               Y6 |  -.2705386    .079922    -3.39   0.001    -.4288199   -.1122574
               Y7 |  -.3535943   .0665697    -5.31   0.000    -.4854321   -.2217564
               Y8 |  -.3303459   .0598154    -5.52   0.000    -.4488071   -.2118846
               Y9 |  -.2789296    .080869    -3.45   0.001    -.4390864   -.1187728
              Y10 |  -.2845711   .0691764    -4.11   0.000    -.4215712   -.1475709
              Y11 |  -.3427876   .0653582    -5.24   0.000    -.4722261   -.2133491
              Y12 |  -.2890109   .0814215    -3.55   0.001    -.4502619   -.1277599
              Y13 |   -.348087   .0726223    -4.79   0.000    -.4919116   -.2042623
              Y14 |  -.3470046   .0619852    -5.60   0.000    -.4697631   -.2242462
              Y15 |  -.2623429    .067987    -3.86   0.000    -.3969875   -.1276983
              Y16 |  -.3702912   .0668467    -5.54   0.000    -.5026776   -.2379048
              Y17 |  -.2229941   .0649739    -3.43   0.001    -.3516714   -.0943168
              Y18 |    -.29555   .0688222    -4.29   0.000    -.4318487   -.1592513
              Y19 |  -.3382327   .0685049    -4.94   0.000     -.473903   -.2025624
              Y20 |  -.3195577    .066625    -4.80   0.000     -.451505   -.1876104
              Y21 |  -.3115878   .0642666    -4.85   0.000    -.4388644   -.1843111
              Y22 |  -.3485407   .0629458    -5.54   0.000    -.4732016   -.2238799
              Y23 |  -.3310524   .0564806    -5.86   0.000    -.4429092   -.2191955
              Y24 |  -.2986675   .0569695    -5.24   0.000    -.4114926   -.1858423
              Y25 |  -.2538659   .0748763    -3.39   0.001    -.4021545   -.1055772
              Y26 |   -.307063   .0623372    -4.93   0.000    -.4305186   -.1836075
              Y27 |  -.2984521   .0555488    -5.37   0.000    -.4084635   -.1884407
              Y28 |  -.2829092   .0672344    -4.21   0.000    -.4160635    -.149755
              Y29 |  -.2692918   .0582456    -4.62   0.000    -.3846441   -.1539395
              Y30 |  -.3390135   .0576276    -5.88   0.000    -.4531419    -.224885
              Y31 |  -.2371845    .073637    -3.22   0.002    -.3830187   -.0913504
              Y32 |  -.2973648   .0703586    -4.23   0.000    -.4367063   -.1580232
              Y33 |  -.2671731   .0678524    -3.94   0.000    -.4015512    -.132795
              Y34 |  -.2880057   .0750136    -3.84   0.000    -.4365663   -.1394451
              Y35 |   -.291353   .0723402    -4.03   0.000    -.4346189    -.148087
              Y36 |  -.2925135   .0843103    -3.47   0.001    -.4594857   -.1255413
              Y37 |  -.2754685   .0758372    -3.63   0.000    -.4256601   -.1252768
              Y38 |  -.3089387   .0720539    -4.29   0.000    -.4516376   -.1662398
              Y39 |  -.1620981   .0920292    -1.76   0.081    -.3443571     .020161
              Y40 |  -.2641791   .0604152    -4.37   0.000    -.3838284   -.1445299
              Y41 |  -.2075703   .0553767    -3.75   0.000    -.3172409   -.0978997
              Y42 |  -.2749097   .0643493    -4.27   0.000    -.4023501   -.1474693
              Y43 |  -.2597607   .0613014    -4.24   0.000    -.3811649   -.1383564
              Y44 |  -.2193347   .0731626    -3.00   0.003    -.3642294   -.0744399
              Y45 |  -.2816636   .0432448    -6.51   0.000    -.3673076   -.1960196
              Y46 |  -.2736998   .0456803    -5.99   0.000    -.3641672   -.1832324
              Y47 |  -.2953348   .0500655    -5.90   0.000    -.3944868   -.1961827
              Y48 |  -.2391598   .0540036    -4.43   0.000    -.3461111   -.1322085
              Y49 |  -.2694291   .0499145    -5.40   0.000    -.3682823    -.170576
              Y50 |  -.2800711   .0630837    -4.44   0.000     -.405005   -.1551372
              Y51 |   -.340827   .0520183    -6.55   0.000    -.4438464   -.2378075
              Y52 |  -.2050176   .0594331    -3.45   0.001    -.3227217   -.0873136
              Y53 |  -.2434318     .04974    -4.89   0.000    -.3419393   -.1449243
              Y54 |  -.2864559     .05152    -5.56   0.000    -.3884886   -.1844232
              Y55 |  -.2087209   .0576451    -3.62   0.000    -.3228841   -.0945577
              Y56 |  -.2462238   .0487465    -5.05   0.000    -.3427636    -.149684
              Y57 |  -.2165571   .0478206    -4.53   0.000    -.3112633   -.1218509
              Y58 |  -.2382367   .0510763    -4.66   0.000    -.3393905   -.1370829
              Y59 |   -.250419   .0504612    -4.96   0.000    -.3503548   -.1504831
              Y60 |  -.2157648   .0487253    -4.43   0.000    -.3122627    -.119267
              Y61 |  -.2728572   .0469014    -5.82   0.000    -.3657429   -.1799715
              Y62 |  -.1969572   .0476339    -4.13   0.000    -.2912937   -.1026208
              Y63 |  -.2340907   .0451534    -5.18   0.000    -.3235146   -.1446668
              Y64 |  -.2051921   .0477565    -4.30   0.000    -.2997713   -.1106128
              Y65 |  -.1948521    .045847    -4.25   0.000    -.2856497   -.1040545
              Y66 |   -.199554   .0405721    -4.92   0.000    -.2799049    -.119203
              Y67 |  -.1821412   .0474808    -3.84   0.000    -.2761745   -.0881079
              Y68 |  -.1771378   .0450127    -3.94   0.000    -.2662831   -.0879925
              Y69 |  -.1641124   .0486167    -3.38   0.001    -.2603953   -.0678295
              Y70 |  -.1654143   .0427917    -3.87   0.000    -.2501609   -.0806676
              Y71 |  -.1253679    .039931    -3.14   0.002    -.2044492   -.0462867
              Y72 |  -.1712905   .0404815    -4.23   0.000     -.251462   -.0911191
              Y73 |  -.1197671    .039021    -3.07   0.003    -.1970461   -.0424881
              Y74 |  -.1482944   .0360504    -4.11   0.000    -.2196904   -.0768984
              Y75 |  -.1110509   .0416442    -2.67   0.009    -.1935249   -.0285768
              Y76 |  -.0945658   .0371135    -2.55   0.012    -.1680671   -.0210645
              Y77 |  -.0907102   .0424447    -2.14   0.035    -.1747697   -.0066507
              Y78 |  -.0946849   .0369905    -2.56   0.012    -.1679427   -.0214271
              Y79 |  -.0856108   .0363359    -2.36   0.020    -.1575722   -.0136493
              Y80 |  -.0338299   .0340603    -0.99   0.323    -.1012844    .0336247
              Y81 |  -.0323936   .0344224    -0.94   0.349    -.1005654    .0357782
              Y82 |  -.0872858   .0351534    -2.48   0.014    -.1569053   -.0176663
              Y83 |  -.0391669   .0348135    -1.13   0.263    -.1081132    .0297795
              Y84 |  -.0281863   .0365377    -0.77   0.442    -.1005473    .0441747
              Y85 |   .0126541   .0330494     0.38   0.703    -.0527985    .0781067
              Y86 |  -.0291889    .032526    -0.90   0.371     -.093605    .0352271
              Y87 |  -.0199852   .0274945    -0.73   0.469    -.0744367    .0344663
              Y88 |          0  (omitted)
            _cons |   .5337994   .0433881    12.30   0.000     .4478716    .6197272
------------------+----------------------------------------------------------------
       state_city |   absorbed                                     (118 categories)
.         
.         
.         
.         quietly {
---------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------
      name:  <unnamed>
       log:  /Users/pamelaban/Dropbox/newspapers_power/PSRM_replication/dataverse_upload/log_stata_files.log
  log type:  text
 opened on:  19 Apr 2017, 14:53:32
.         
.         **produce dta for graph
.         use `tmp1', clear
.         
.         gen city_manager_govt = (year >= first_year)
.         
.         gen t = year - first_year + .5
(21,451 missing values generated)
.         
.         foreach z in "mayor" "city_manager" "city_council" {
  2.                 egen mean_`z'_tmp = sum(`z') if city_manager_govt == 0, by(year)
  3.                 egen control_mean_`z' = max(mean_`z'_tmp), by(year)
  4.                 drop mean_`z'_tmp
  5.         }
(3256 missing values generated)
(3256 missing values generated)
(3256 missing values generated)
.         
.         drop if state == ""
(0 observations deleted)
.         
.         collapse (sum) mayor city_manager city_council mayor_x city_manager_x city_council_x control_*, by(state city t)
.         
.         egen total   = rsum(mayor   city_manager   city_council)
.         egen total_x = rsum(mayor_x city_manager_x city_council_x)
.         egen total_control = rsum(control_mean_mayor control_mean_city_manager control_mean_city_council)
.         
.         keep if t >= -20 & t <= 20
(5,826 observations deleted)
.         
.         foreach i of varlist mayor city_manager city_council {
  2.           gen r_`i'  = `i'/total if total >= 50
  3.         }
(2,106 missing values generated)
(2,106 missing values generated)
(2,106 missing values generated)
.         
.         foreach i of varlist mayor_x city_manager_x city_council_x {
  2.           gen r_`i'  = `i'/total_x if total_x >= 10
  3.         }
(2,469 missing values generated)
(2,469 missing values generated)
(2,469 missing values generated)
.         
.         foreach i of varlist mayor city_manager city_council {
  2.           gen r_control_`i'  = control_mean_`i'/total_control if total_control >= 50
  3.         }
.         
.         egen xmin = min(t), by(state city)
.         egen xmax = max(t), by(state city)
.         sort state city t
.         list state city if city != city[_n-1] & xmin < 0 & xmax > 0, noo nod

  +---------------------------+
  | state                city |
  |---------------------------|
  |    AK           FAIRBANKS |
  |    AL            ANNISTON |
  |    AR              CAMDEN |
  |    AR        FAYETTEVILLE |
  |    AR                HOPE |
  |---------------------------|
  |    AZ         CASA GRANDE |
  |    AZ           FLAGSTAFF |
  |    AZ             PHOENIX |
  |    AZ                YUMA |
  |    CA             ARCADIA |
  |---------------------------|
  |    CA         BAKERSFIELD |
  |    CA              EUREKA |
  |    CA              FRESNO |
  |    CA             OAKLAND |
  |    CA              OXNARD |
  |---------------------------|
  |    CA          SANTA CRUZ |
  |    CO             GREELEY |
  |    FL               MIAMI |
  |    FL         PANAMA CITY |
  |    IL               ALTON |
  |---------------------------|
  |    IL   ARLINGTON HEIGHTS |
  |    IL          CARBONDALE |
  |    IL           GALESBURG |
  |    IL        MOUNT VERNON |
  |    IL            PALATINE |
  |---------------------------|
  |    KS            ATCHISON |
  |    KS          BELLEVILLE |
  |    KS             EMPORIA |
  |    KS                HAYS |
  |    KS          HUTCHINSON |
  |---------------------------|
  |    KS             KINSLEY |
  |    KS             WICHITA |
  |    KS            WINFIELD |
  |    MI            ESCANABA |
  |    MI             HOLLAND |
  |---------------------------|
  |    MI            IRONWOOD |
  |    MI           LUDINGTON |
  |    MI        SAINT JOSEPH |
  |    MI       TRAVERSE CITY |
  |    MN              WINONA |
  |---------------------------|
  |    MO              JOPLIN |
  |    MO         KANSAS CITY |
  |    MO           MARYVILLE |
  |    MO              MEXICO |
  |    MO             MOBERLY |
  |---------------------------|
  |    MO              NEOSHO |
  |    MT              HELENA |
  |    NC           ASHEVILLE |
  |    NC          BURLINGTON |
  |    NC         CHAPEL HILL |
  |---------------------------|
  |    NC              DURHAM |
  |    NC            GASTONIA |
  |    NC           GOLDSBORO |
  |    NC          GREENSBORO |
  |    NC      HENDERSONVILLE |
  |---------------------------|
  |    NC             HICKORY |
  |    NC          HIGH POINT |
  |    NC           LUMBERTON |
  |    NC           MORGANTON |
  |    NC          REIDSVILLE |
  |---------------------------|
  |    NC           SALISBURY |
  |    NC         STATESVILLE |
  |    NC         THOMASVILLE |
  |    NC          WILMINGTON |
  |    NE            ALLIANCE |
  |---------------------------|
  |    NH          PORTSMOUTH |
  |    NM         ALBUQUERQUE |
  |    NM             CLAYTON |
  |    NM              CLOVIS |
  |    NV                RENO |
  |---------------------------|
  |    NY         CANANDAIGUA |
  |    NY                TROY |
  |    OH            HAMILTON |
  |    OH               PIQUA |
  |    OH          PORTSMOUTH |
  |---------------------------|
  |    OH            SANDUSKY |
  |    OH               XENIA |
  |    OK                 ADA |
  |    OK              LAWTON |
  |    OK            MUSKOGEE |
  |---------------------------|
  |    OK            SALLISAW |
  |    OR               SALEM |
  |    PA             ALTOONA |
  |    PA            OIL CITY |
  |    PA           POTTSTOWN |
  |---------------------------|
  |    RI             NEWPORT |
  |    SC           GREENWOOD |
  |    SC              SUMTER |
  |    TX             ABILENE |
  |    TX            AMARILLO |
  |---------------------------|
  |    TX              BONHAM |
  |    TX         BROWNSVILLE |
  |    TX               BRYAN |
  |    TX      CORPUS CHRISTI |
  |    TX             DEL RIO |
  |---------------------------|
  |    TX              DENTON |
  |    TX           GALVESTON |
  |    TX           HARLINGEN |
  |    TX             LUBBOCK |
  |    TX               MEXIA |
  |---------------------------|
  |    TX              ODESSA |
  |    TX               PARIS |
  |    TX         SAN ANTONIO |
  |    TX              TAYLOR |
  |    TX                WACO |
  |---------------------------|
  |    TX          WAXAHACHIE |
  |    UT               OGDEN |
  |    UT               PROVO |
  |    VA            DANVILLE |
  |    VA            STAUNTON |
  |---------------------------|
  |    WI          JANESVILLE |
  |    WI             MADISON |
  |    WI             OSHKOSH |
  |    WI         RHINELANDER |
  |    WV           BLUEFIELD |
  +---------------------------+
.         
.         gen rel_mayor_council_total = r_city_council + r_mayor
(2,106 missing values generated)
.         gen rel_mayor_council_control_total = r_control_city_council + r_control_mayor
.         gen rel_mayor_council = r_mayor / rel_mayor_council_total
(2,106 missing values generated)
.         gen rel_mayor_council_control = r_control_mayor / rel_mayor_council_control_total
.         
.         gen rel_mayor_council_total_x = r_city_council_x + r_mayor
(2,590 missing values generated)
.         gen rel_mayor_council_x = r_mayor_x / rel_mayor_council_total_x  
(2,590 missing values generated)
.         
.         collapse (mean) r_mayor r_city_manager r_city_council r_mayor_x r_city_manager_x r_city_council_x r_control* rel_mayor_council rel_mayor_council_control rel_mayor_council_x (semean) r_mayor_sd=r_mayor r_city_manager_sd=r_city_manager r_city_coun
> cil_sd=r_city_council r_mayor_x_sd=r_mayor_x r_city_manager_x_sd=r_city_manager_x r_city_council_x_sd=r_city_council_x r_control_mayor_sd=r_control_mayor r_control_city_council_sd=r_control_city_council r_control_city_manager_sd=r_control_city_manager r
> el_mayor_council_sd=rel_mayor_council rel_mayor_council_control_sd=rel_mayor_council_control rel_mayor_council_x_sd=rel_mayor_council_x, by(t)
.         
.         saveold for_mayor_r_graph, replace version(12)
(saving in Stata 12 format, which can be read by Stata 11 or 12)
(note: file for_mayor_r_graph.dta not found)
file for_mayor_r_graph.dta saved
. }

. 
. 
. 
. 
. ********************************************************************************
. * Relative Coverage of Congress in Tariff Policymaking
. ********************************************************************************
. 
. if `make_rtaa'==1 {
.         use hits_tariff, clear
.         keep if year >= 1880 & year<=1975
(394 observations deleted)
.         
.         gen t = year - mod(year,5)
.         collapse (sum) cong pres, by(t)
.         
.         gen r = cong/(cong+pres)
.         
.         reg r t if t < 1932.5

      Source |       SS           df       MS      Number of obs   =        11
-------------+----------------------------------   F(1, 9)         =      0.06
       Model |  .000424596         1  .000424596   Prob > F        =    0.8045
    Residual |  .058820338         9  .006535593   R-squared       =    0.0072
-------------+----------------------------------   Adj R-squared   =   -0.1031
       Total |  .059244934        10  .005924493   Root MSE        =    .08084

------------------------------------------------------------------------------
           r |      Coef.   Std. Err.      t    P>|t|     [95% Conf. Interval]
-------------+----------------------------------------------------------------
           t |   .0003929   .0015416     0.25   0.805    -.0030944    .0038803
       _cons |  -.1839297   2.936879    -0.06   0.951    -6.827612    6.459753
------------------------------------------------------------------------------
.         predict r1 if e(sample)
(option xb assumed; fitted values)
(9 missing values generated)
.         
.         reg r t if t > 1932.5

      Source |       SS           df       MS      Number of obs   =         9
-------------+----------------------------------   F(1, 7)         =      0.09
       Model |  .000160055         1  .000160055   Prob > F        =    0.7766
    Residual |  .012880081         7  .001840012   R-squared       =    0.0123
-------------+----------------------------------   Adj R-squared   =   -0.1288
       Total |  .013040136         8  .001630017   Root MSE        =     .0429

------------------------------------------------------------------------------
           r |      Coef.   Std. Err.      t    P>|t|     [95% Conf. Interval]
-------------+----------------------------------------------------------------
           t |   .0003267   .0011076     0.29   0.777    -.0022923    .0029456
       _cons |  -.1916989   2.165314    -0.09   0.932    -5.311853    4.928455
------------------------------------------------------------------------------
.         predict r2 if e(sample)
(option xb assumed; fitted values)
(11 missing values generated)
.         
.         scatter r r1 r2 t , xline(1932.5) s(i i i) mlab(t) mlabpos(0) mlabsize(*.80) c(. l l) legend(off) xtitle(Period) ytitle(Relative Mentions of Cong vs. Pres on Tariff)
.         
.         gen tt = t - 1935
.         gen post = tt >= 0 if tt != .
.         gen post_tt = post * tt
.         
.         reg r post

      Source |       SS           df       MS      Number of obs   =        20
-------------+----------------------------------   F(1, 18)        =     17.08
       Model |  .068574736         1  .068574736   Prob > F        =    0.0006
    Residual |   .07228507        18  .004015837   R-squared       =    0.4868
-------------+----------------------------------   Adj R-squared   =    0.4583
       Total |  .140859806        19  .007413674   Root MSE        =    .06337

------------------------------------------------------------------------------
           r |      Coef.   Std. Err.      t    P>|t|     [95% Conf. Interval]
-------------+----------------------------------------------------------------
        post |  -.1177008    .028483    -4.13   0.001    -.1775413   -.0578603
       _cons |   .5646128    .019107    29.55   0.000     .5244706    .6047551
------------------------------------------------------------------------------
.         reg r post

      Source |       SS           df       MS      Number of obs   =        20
-------------+----------------------------------   F(1, 18)        =     17.08
       Model |  .068574736         1  .068574736   Prob > F        =    0.0006
    Residual |   .07228507        18  .004015837   R-squared       =    0.4868
-------------+----------------------------------   Adj R-squared   =    0.4583
       Total |  .140859806        19  .007413674   Root MSE        =    .06337

------------------------------------------------------------------------------
           r |      Coef.   Std. Err.      t    P>|t|     [95% Conf. Interval]
-------------+----------------------------------------------------------------
        post |  -.1177008    .028483    -4.13   0.001    -.1775413   -.0578603
       _cons |   .5646128    .019107    29.55   0.000     .5244706    .6047551
------------------------------------------------------------------------------
.         reg r post tt

      Source |       SS           df       MS      Number of obs   =        20
-------------+----------------------------------   F(2, 17)        =      8.20
       Model |  .069155123         2  .034577562   Prob > F        =    0.0032
    Residual |  .071704683        17  .004217923   R-squared       =    0.4910
-------------+----------------------------------   Adj R-squared   =    0.4311
       Total |  .140859806        19  .007413674   Root MSE        =    .06495

------------------------------------------------------------------------------
           r |      Coef.   Std. Err.      t    P>|t|     [95% Conf. Interval]
-------------+----------------------------------------------------------------
        post |  -.1361779   .0577342    -2.36   0.031    -.2579864   -.0143695
          tt |   .0003695   .0009962     0.37   0.715    -.0017323    .0024714
       _cons |   .5756991   .0357303    16.11   0.000     .5003148    .6510835
------------------------------------------------------------------------------
.         reg r post tt post_tt

      Source |       SS           df       MS      Number of obs   =        20
-------------+----------------------------------   F(3, 16)        =      5.14
       Model |  .069159387         3  .023053129   Prob > F        =    0.0111
    Residual |  .071700419        16  .004481276   R-squared       =    0.4910
-------------+----------------------------------   Adj R-squared   =    0.3955
       Total |  .140859806        19  .007413674   Root MSE        =    .06694

------------------------------------------------------------------------------
           r |      Coef.   Std. Err.      t    P>|t|     [95% Conf. Interval]
-------------+----------------------------------------------------------------
        post |   -.136022   .0597237    -2.28   0.037    -.2626305   -.0094135
          tt |   .0003929   .0012765     0.31   0.762    -.0023132    .0030991
     post_tt |  -.0000663   .0021487    -0.03   0.976    -.0046214    .0044888
       _cons |   .5764009   .0432896    13.32   0.000     .4846311    .6681707
------------------------------------------------------------------------------
.         
.         saveold for_tariff_r_graph, version(12) replace
(saving in Stata 12 format, which can be read by Stata 11 or 12)
(note: file for_tariff_r_graph.dta not found)
file for_tariff_r_graph.dta saved
. }

. 
. 
. 
. ********************************************************************************
. * Party Committee Power Over Time in Nine U.S. States
. ********************************************************************************
. 
. 
. if `make_party_committee' == 1 {
.         
.         use hits_candidates.dta, clear
.         merge 1:1 state year using hits_partycommittee
(note: variable state was str2, now str15 to accommodate using data's values)

    Result                           # of obs.
    -----------------------------------------
    not matched                           426
        from master                         1  (_merge==1)
        from using                        425  (_merge==2)

    matched                             4,194  (_merge==3)
    -----------------------------------------
.         keep if _merge==3
(426 observations deleted)
.         drop _merge
.         drop if state == "DC" | state =="V" | state=="Victoria" | state=="NSW" | state=="New South Wales"
(46 observations deleted)
.         * drop obs for states before they were admitted to the union
.         drop if state == "ND" & year < 1889
(12 observations deleted)
.         drop if state == "SD" & year < 1889
(10 observations deleted)
.         drop if state == "MT" & year < 1889
(12 observations deleted)
.         drop if state == "WA" & year < 1888
(8 observations deleted)
.         drop if state == "ID" & year < 1900
(9 observations deleted)
.         drop if state == "WY" & year < 1900
(0 observations deleted)
.         drop if state == "UT" & year < 1896
(17 observations deleted)
.         drop if state == "OK" & year < 1907
(27 observations deleted)
.         drop if state == "AZ" & year < 1912
(35 observations deleted)
.         drop if state == "NM" & year < 1912
(31 observations deleted)
.         drop if state == "AK" & year < 1958
(33 observations deleted)
.         drop if state == "HI" & year < 1958
(46 observations deleted)
.         
.         merge m:1 state using Mayhew_TPO_Scores

    Result                           # of obs.
    -----------------------------------------
    not matched                             2
        from master                         0  (_merge==1)
        from using                          2  (_merge==2)

    matched                             3,908  (_merge==3)
    -----------------------------------------
.         drop if _merge==2
(2 observations deleted)
.         drop _merge     
.         
.         gen n_othr = democ_othr + repub_othr
.         gen party_power = 100*n_othr/(n_othr + candidate_hits) if candidate_hits>500
(1,067 missing values generated)
. 
.         egen x = mean(party_power), by(state)
(22 missing values generated)
.         gen party_power_norm = party_power/x
(1,067 missing values generated)
.         
.         keep state year party_power_norm
.         saveold for_partycommittee_r_graph, version(12) replace
(saving in Stata 12 format, which can be read by Stata 11 or 12)
(note: file for_partycommittee_r_graph.dta not found)
file for_partycommittee_r_graph.dta saved
. }

. 
. log close
      name:  <unnamed>
       log:  /Users/pamelaban/Dropbox/newspapers_power/PSRM_replication/dataverse_upload/log_stata_files.log
  log type:  text
 closed on:  19 Apr 2017, 14:53:35
---------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------
---------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------
      name:  <unnamed>
       log:  /Users/pamelaban/Dropbox/newspapers_power/PSRM_replication/dataverse_upload/log_stata_files.log
  log type:  text
 opened on:  19 Apr 2017, 14:53:44

. 
. ********************************************************************************
. * This do-file replicates the appendix tables and figures in
. * "How Newspapers Reveal Political Power" (Ban, Fouirnaies, Hall, Snyder)
. *
. * Produces:
. * Table A.1: Number of Pages, Newspapers, and Counties in Dataset by State
. * Table A.2: The 50 Most Common Newspapers in Dataset
. * Figure A.1: Geographical Distribution of Pages and Newspapers in Dataset
. * Figure A.2: Yearly Number of Pages in Sample by Region
. *
. * November 20, 2016
. ********************************************************************************
. 
. set more off

.         
. ********************************************************************************
. * Table A.1: Number of Pages, Newspapers, and Counties in Dataset by State     
. ********************************************************************************
. 
. use DescriptiveStatsMetadata_25years, clear

. replace pages = pages/1000
(190 real changes made)

. reshape wide pages newspapers counties, i(state) j(period)
(note: j = 1877 1902 1927 1952)

Data                               long   ->   wide
-----------------------------------------------------------------------------
Number of obs.                      190   ->      50
Number of variables                   5   ->      13
j variable (4 values)            period   ->   (dropped)
xij variables:
                                  pages   ->   pages1877 pages1902 ... pages1952
                             newspapers   ->   newspapers1877 newspapers1902 ... newspapers1952
                               counties   ->   counties1877 counties1902 ... counties1952
-----------------------------------------------------------------------------

. foreach var of varlist pages* newspapers* counties* {
  2.         replace `var' = 0  if `var'==.
  3. }
(2 real changes made)
(1 real change made)
(4 real changes made)
(3 real changes made)
(2 real changes made)
(1 real change made)
(4 real changes made)
(3 real changes made)
(2 real changes made)
(1 real change made)
(4 real changes made)
(3 real changes made)

. sort state

. 
. quietly {
---------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------
      name:  <unnamed>
       log:  /Users/pamelaban/Dropbox/newspapers_power/PSRM_replication/dataverse_upload/log_stata_files.log
  log type:  text
 opened on:  19 Apr 2017, 14:53:44

. 
. ********************************************************************************
. * Table A.2: The 50 Most Common Newspapers in Dataset
. ********************************************************************************
. 
. use newspaper_summary, clear

. 
. drop if regexm(newspaper, "Winnipeg") == 1
(1 observation deleted)

. 
. sum min_year

    Variable |        Obs        Mean    Std. Dev.       Min        Max
-------------+---------------------------------------------------------
    min_year |      3,146    1892.496    36.48654       1700       2009

. local m = r(N)

. 
. replace newspaper = regexr(newspaper, "\&", "\&")
(3 real changes made)

. 
. *** rank in terms of occurrences
. gsort -occurrences

. gen rank = _n

. 
. *** get rid of "The" for sorting alphabetically
. gen newspaper_sort_name = newspaper

. replace newspaper_sort_name = regexr(newspaper, "^The ", "")
(1,756 real changes made)

. sort newspaper_sort_name

. 
. *** table can only fit top 50
. keep if rank <= 50
(3,096 observations deleted)

. 
. cap log close
